DevOps/MLOps Engineer

This is a fully remote position, open to applicants anywhere in the world.

📋 Description

• Design and implement CI/CD pipelines for both standard applications and machine learning workflows.

• Build and sustain scalable infrastructure utilizing containerization technologies (Docker, Kubernetes) and infrastructure-as-code tools.

• Manage and optimize cloud environments across AWS, GCP, or Azure.

• Deploy, monitor, and maintain machine learning models and pipelines in production settings.

• Establish and enhance monitoring, logging, and alerting systems to ensure the health of application and ML infrastructure.

• Automate deployment, scaling, and operational tasks to minimize manual effort and improve reliability.

• Collaborate with software engineers and data scientists to comprehend deployment needs and refine system architecture.

• Ensure security, performance, and reliability throughout infrastructure and application layers.

• Troubleshoot and resolve issues related to infrastructure, deployment, and operations.

• Maintain documentation and establish best practices for infrastructure and deployment processes.

⛳️ Requirements

• Over 3 years of practical experience in DevOps or infrastructure engineering.

• Strong expertise in containerization and orchestration technologies (Docker, Kubernetes).

• Experience with infrastructure-as-code tools such as Terraform, CloudFormation, or Ansible.

• Comprehensive understanding of CI/CD principles and practical experience with tools like Jenkins, GitLab CI/CD, GitHub Actions, or similar.

• Strong programming abilities in Python, Bash, or Go for automation and scripting purposes.

• Practical experience with at least one major cloud platform (AWS, GCP, or Azure).

• Familiarity with monitoring and logging solutions (Prometheus, ELK stack, CloudWatch, or similar).

• Experience with ML operationalization and deployment platforms such as MLflow, Kubeflow, SageMaker, or Vertex AI is a plus.

• Understanding of ML frameworks and workflows (TensorFlow, PyTorch, scikit-learn) is advantageous.

• Strong problem-solving capabilities and the ability to work independently on infrastructure challenges.

🏝️ Benefits

• Paid training and additional professional development opportunities.

• Flexibility and freedom to work remotely from any location with a stable internet connection.

• A steady stream of engaging and challenging work to undertake.

• Annual salary increases based on performance excellence.